Reconstructing transmission trees for communicable diseases using densely sampled genetic data
Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting w...
| Main Authors: | , , , , , , , |
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| Format: | Article |
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Institute of Mathematical Statistics
2016
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| Online Access: | https://eprints.nottingham.ac.uk/42771/ |
| _version_ | 1848796563671875584 |
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| author | Worby, Colin J. O'Neill, Philip D. Kypraios, Theodore Robotham, Julie V. De Angelis, Daniela Cartwright, Edward J.P. Peacock, Sharon J. Cooper, Ben S. |
| author_facet | Worby, Colin J. O'Neill, Philip D. Kypraios, Theodore Robotham, Julie V. De Angelis, Daniela Cartwright, Edward J.P. Peacock, Sharon J. Cooper, Ben S. |
| author_sort | Worby, Colin J. |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation. |
| first_indexed | 2025-11-14T19:49:58Z |
| format | Article |
| id | nottingham-42771 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:49:58Z |
| publishDate | 2016 |
| publisher | Institute of Mathematical Statistics |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-427712020-05-04T17:40:37Z https://eprints.nottingham.ac.uk/42771/ Reconstructing transmission trees for communicable diseases using densely sampled genetic data Worby, Colin J. O'Neill, Philip D. Kypraios, Theodore Robotham, Julie V. De Angelis, Daniela Cartwright, Edward J.P. Peacock, Sharon J. Cooper, Ben S. Whole genome sequencing of pathogens from multiple hosts in an epidemic offers the potential to investigate who infected whom with unparalleled resolution, potentially yielding important insights into disease dynamics and the impact of control measures. We considered disease outbreaks in a setting with dense genomic sampling, and formulated stochastic epidemic models to investigate person-to-person transmission, based on observed genomic and epidemiological data. We constructed models in which the genetic distance between sampled genotypes depends on the epidemiological relationship between the hosts. A data augmented Markov chain Monte Carlo algorithm was used to sample over the transmission trees, providing a posterior probability for any given transmission route. We investigated the predictive performance of our methodology using simulated data, demonstrating high sensitivity and specificity, particularly for rapidly mutating pathogens with low transmissibility. We then analyzed data collected during an outbreak of methicillin-resistant Staphylococcus aureus in a hospital, identifying probable transmission routes and estimating epidemiological parameters. Our approach overcomes limitations of previous methods, providing a framework with the flexibility to allow for unobserved infection times, multiple independent introductions of the pathogen, and within-host genetic diversity, as well as allowing forward simulation. Institute of Mathematical Statistics 2016-03-25 Article PeerReviewed Worby, Colin J., O'Neill, Philip D., Kypraios, Theodore, Robotham, Julie V., De Angelis, Daniela, Cartwright, Edward J.P., Peacock, Sharon J. and Cooper, Ben S. (2016) Reconstructing transmission trees for communicable diseases using densely sampled genetic data. Annals of Applied Statistics, 10 (1). pp. 395-417. ISSN 1941-7330 Bayesian inference Infectious disease Epidemics Outbreak investigation Transmission routes http://projecteuclid.org/euclid.aoas/1458909921#info doi:10.1214/15-AOAS898 doi:10.1214/15-AOAS898 |
| spellingShingle | Bayesian inference Infectious disease Epidemics Outbreak investigation Transmission routes Worby, Colin J. O'Neill, Philip D. Kypraios, Theodore Robotham, Julie V. De Angelis, Daniela Cartwright, Edward J.P. Peacock, Sharon J. Cooper, Ben S. Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_full | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_fullStr | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_full_unstemmed | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_short | Reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| title_sort | reconstructing transmission trees for communicable diseases using densely sampled genetic data |
| topic | Bayesian inference Infectious disease Epidemics Outbreak investigation Transmission routes |
| url | https://eprints.nottingham.ac.uk/42771/ https://eprints.nottingham.ac.uk/42771/ https://eprints.nottingham.ac.uk/42771/ |